





'''simple docstring'''

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       Tuple:

   '''simple docstring'''







   if isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ) and isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							len(set_a.intersection(__UpperCAmelCase   )   )

      if alternative_union:
         __SCREAMING_SNAKE_CASE   				=							len(__UpperCAmelCase   ) + len(__UpperCAmelCase   )
      else:
         __SCREAMING_SNAKE_CASE   				=							len(set_a.union(__UpperCAmelCase   )   )

      return intersection / union

   if isinstance(__UpperCAmelCase				,						(list, tuple)   ) and isinstance(__UpperCAmelCase				,						(list, tuple)   ):
      __SCREAMING_SNAKE_CASE   				=							[element for element in set_a if element in set_b]

      if alternative_union:
         __SCREAMING_SNAKE_CASE   				=							len(__UpperCAmelCase   ) + len(__UpperCAmelCase   )
         return len(__UpperCAmelCase   ) / union
      else:
         __SCREAMING_SNAKE_CASE   				=							set_a + [element for element in set_b if element not in set_a]
         return len(__UpperCAmelCase   ) / len(__UpperCAmelCase   )

      return len(__UpperCAmelCase   ) / len(__UpperCAmelCase   )
   return None


if __name__ == "__main__":
    a   	=    {"a", "b", "c", "d", "e"}
    a   	=    {"c", "d", "e", "f", "h", "i"}
    print(jaccard_similarity(set_a, set_b))






"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
